This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning
model pretrained on narrated videos which are readily-available at scale. The Vid2Seq …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Video-text retrieval has been a crucial and fundamental task in multi-modal research. The
development of video-text retrieval has been considerably promoted by large-scale multi …

Abstract Mainstream Video-Language Pre-training models consist of three parts, a video
encoder, a text encoder, and a video-text fusion Transformer. They pursue better …

Video-language pre-training (VLP) has become increasingly important due to its ability to
generalize to various vision and language tasks. However, existing egocentric VLP …

Understanding verbs is crucial to modelling how people and objects interact with each other
and the environment through space and time. Recently, state-of-the-art video-language …

Contrastive learning-based video-language representation learning approaches, eg, CLIP,
have achieved outstanding performance, which pursue semantic interaction upon pre …

Text-Video retrieval is a task of great practical value and has received increasing attention,
among which learning spatial-temporal video representation is one of the research hotspots …

A great challenge in video-language (VidL) modeling lies in the disconnection between
fixed video representations extracted from image/video understanding models and …